Short-rotation coppices (SRCs) play an increasingly important role in sustainable fast-growing wood biomass production, offering rapid returns while contributing to climate change mitigation and reducing pressure on natural forests. Traditional field measurements of tree parameters in SRC plantations are time-consuming and labour-intensive, creating a bottleneck in efficient plantation management. While various remote sensing technologies exist, UAV-mounted LiDAR systems offer unique advantages for SRC monitoring through high precision and operational flexibility, yet their application in SRC contexts remains understudied.

We explore the potential of UAV LiDAR for tree diameter at breast height (DBH) estimation in SRC plantations by systematically investigating point cloud feature extraction methods and comparing predictive models. Working on a 1-hectare plot at the Leibniz Institute for Agricultural Engineering and Bioeconomy in Potsdam, Germany, we deployed a RIEGL miniVUX-1UAV scanner mounted on a DJI M600 platform to collect high-density point cloud data (2227 pt/m²). Manual DBH measurements and geolocation data from 500 trees were collected to validate our estimation models. Our results demonstrate the most suitable LiDAR metric combinations and model architectures for tree diameter estimation and contribute to understanding the applicability of UAV LiDAR technology in rapid SRC inventory assessment.